Module 02360 (2004)
Syllabus page 2004/2005
06-02360
Introduction to Neural Networks
Level 2/I
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Relevant Links
See Neural Networks
Web-page for module material and further useful links.
Outline
This module provides an introduction to basic neurobiology, discusses the main neural network architectures and learning algorithms, and presents a number of neural network applications. Particular models covered include McCulloch Pitts Neurons, Single Layer Perceptrons, Multi-Layer Perceptrons, Radial Basis Function Networks, Committee Machines, Kohonen Self-Organising Maps, and Learning Vector Quantization.
Aims
The aims of this module are to:
- introduce the main fundamental principles and techniques of neural network systems
- investigate the principal neural network models and applications
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | describe the relation between real brains and simple artificial neural network models | Examination |
| 2 | explain and contrast the most common architectures and learning algorithms for Multi-Layer Perceptrons, Radial-Basis Function Networks, Committee Machines, and Kohonen Self-Organising Maps | Examination |
| 3 | discuss the main factors involved in achieving good learning and generalization performance in neural network systems | Examination, assignment |
| 4 | identify the main implementational issues for common neural network systems | Examination, assignment |
| 5 | evaluate the practical considerations in applying neural networks to real classification and regression problems | Examination, assignment |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
2 hrs of lectures per week plus labs
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- 2 hour examination (70%), continuous assessment (30%). Resit by written examination only with the continuous assessment mark carried forward.
Recommended Books
| Title | Author(s) | Publisher, Date |
| An Introduction to Neural Networks | K Gurney | Routledge, 1997 |
| Neural Networks: A Comprehensive Foundation | S Haykin | Prentice Hall, 1999 |
| Neural Networks for Pattern Recognition | C M Bishop | Oxford University Press, 1995 |
| The Essence of Neural Networks | R Callan | Prentice Hall Europe, 1999 |
| Introduction to Neural Networks | R Beale & T Jackson | IOP Publishing, 1990 |
| An Introduction to the Theory of Neural Computation | J Hertz, A Krogh & R G Palmer | Addison Wesley, 1991 |
| Principles of Neurocomputing for Science and Engineering | F M Ham & I Kostanic | McGraw Hill, 2001 |
Detailed Syllabus
- Introduction to Neural Networks and their History.
- Biological Neurons and Neural Networks. Artificial Neurons.
- Networks of Artificial Neurons. Single Layer Perceptrons.
- Learning and Generalization in Single Layer Perceptrons.
- Hebbian Learning. Gradient Descent Learning.
- The Generalized Delta Rule. Practical Considerations.
- Learning in Multi-Layer Perceptrons. Back-Propagation.
- Learning with Momentum. Conjugate Gradient Learning.
- Bias and Variance. Under-Fitting and Over-Fitting.
- Improving Generalization.
- Applications of Multi-Layer Perceptrons.
- Radial Basis Function Networks: Introduction.
- Radial Basis Function Networks: Algorithms.
- Radial Basis Function Networks: Applications.
- Committee Machines.
- Self Organizing Maps: Fundamentals.
- Self Organizing Maps: Algorithms and Applications.
- Learning Vector Quantisation.
- Overview of More Advanced Topics.
Last updated: 8 December 2003
Source file: /internal/modules/COMSCI/2004/xml/02360.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus